Non‐stationary frequency analysis of extreme streamflow disturbance in a typical ecological function reserve of China under a changing climate

• Published in: Ecohydrology
• Main Authors: Liu, Mengyang, Ma, Xieyao, Yin, Yixing, Zhang, Zengxin, Yin, Jun, Ullah, Irfan, Arshad, Muhammad
• Other Authors: National Natural Science Foundation of China
• Format: Article in Journal/Newspaper
• Language: English
• Published: Wiley 2021
• Subjects: Arctic; Pacific; Climate change; Global warming; North Atlantic; North Atlantic oscillation
• Online Access: http://dx.doi.org/10.1002/eco.2323; https://onlinelibrary.wiley.com/doi/pdf/10.1002/eco.2323; https://onlinelibrary.wiley.com/doi/full-xml/10.1002/eco.2323

Abstract Extreme hydro‐meteorological events occur more frequently with global warming, which have a great impact on ecology, environment and hydrology. Hydro‐meteorological variables often exhibit disturbances of non‐stationary characteristics. In this paper, we take a typical ecological function reserve (Poyang Lake Basin) as an instance to study the trend and probability characters of extreme streamflow based on stationary and non‐stationary generalized extreme value (GEV) models. Time and large‐scale climate factors such as Southern Oscillation (SO), North Atlantic Oscillation (NAO), Arctic Oscillation (AO), Pacific Decadal Oscillation (PDO), Atlantic multidecadal oscillation (AMO), NINO3 SST (NINO3) and NINO3.4 SST (NINO3.4) are incorporated as covariates to establish non‐stationary model. The main results are as follows: (1) Downward trends prevail for maximum streamflow series, but upward trends dominate for minimum streamflow series. (2) The tested optimal models of some maximum streamflow series are non‐stationary, while the optimal models of all minimum streamflow series are non‐stationary. In the 1970s and before (1980s and thereafter), the return periods of optimal model are greater (less) than those of stationary model for most minimum series, and the risks of river flooding and drying up without calculating by optimal model are underestimated (overestimated). (3) The estimated quantiles tend to be larger during La Niña than during El Niño in this study area. Other climate indices also have obvious effects on the estimated quantiles. This study indicates that the influence of climate factors on the extreme streamflow should be paid more attention and non‐stationary modelling will benefit ecological management under climate change.